Last year we got the opportunity to participate in the Running Robot Competition in China. Since we enjoyed getting to participate in it last year, we wanted to attend again this year.

The Running Robot Competition is not a part of the RoboCup tournaments we usually participate in. It is a tournament where Robots have to go through a course, overcoming multiple obstacles to reach their goal. These obstacles are for example having to wait until a barrier has moved out of the way and only then walk past it, or having to climb a few stairs.

Due to Covid-19 we did not get the opportunity to participate in person, but we were glad to participate over the internet. For this purpose, the organizers prepared a simulation environment.

We discussed a few options on how to solve this.
Starting from a more conventional approach to solve the challenges. In this approach, we would start with developing a vision that can e.g. see the yellow and black from the barrier and as soon as it reaches a certain angle, we would program the robot to start walking.

However we wanted to use this opportunity to learn about something that most of our team had less experience with. This is why we decided to use reinforcement learning for our approach.

Reinforcement learning is an approach where we use neural networks so the robot can learn how to overcome the obstacles without us telling it how to solve them. This can lead to more efficient ways of solving the problem than we would have thought of. This could include finding a bug in the physics of the simulator to achieve normally impossible things or something as simple as walking another way that we did not think of.

We used only the robot’s camera image as an input to the reinforcement learning algorithm. Based on this information the robot had to figure out what it had to do now. Does it have to wait for the barrier to rise or is it already up and it can start walking?
This sounds trivial for us as a human, but the robot starts out with no concept of a barrier being an obstacle. In the beginning, it does not know the difference between the ground which it can step on and an obstacle that it should avoid.

To ease the robot to learn these things, we provided points as a reward to the robot if it came closer to the goal and deducted points if it walked into an obstacle like the barrier.

The robot could control the direction in which it walked. We used the same program to let the robot walk as we use on our soccer-playing robots. However since a different robot was used in this competition, we had to change a few parameters. However, we were able to use a script from our existing walking to let the robot learn the best way to walk in this new simulation.

With this approach, we were able to solve the first obstacles. Due to a misunderstanding, we had less time and were not able to solve the full course.

However we still had a lot of fun in developing our approach to solve the problems the Running Robot Competition presented. We also had the opportunity to learn a lot about reinforcement learning.

As you probably have figured out from reading the headline already, we did not only have a lot of fun during the competition, we even managed to finish in third place in the competition!

We want to participate in the world championship of 2020 again. For this application the conditions were a bit different compared to last year.

Instead of writing a Team Description Paper, this year we were required to write an Extended Abstract. In it we described what we learned in the world championship in Sydney, the most important problems we have to address for the next championship and what we want to develop in time for the world championship in Bordeaux. Our Extended Abstract can be found in our publications and here.

We have also created a video which shows the abilities of our robots:

On Tuesday it will start for us. We will be going to the German Open 2019!

The German Open is the German championship in the RoboCup. There are competitions in the 6 Major Leagues, so all kinds of robots, from helping in crisis situations to save lives to our soccer playing robots. 40 teams from 16 countries will be there to fight to win the German Open competitons.

We will get the opportunity to try out and present the changes to our Software and Hardware which we have made since the last world cup in official games. With the new insight we can give the finishing touches to our robots before we fly to the next world cup.

The other teams in our league, which we will play against, are:
WF Wolves (Ostfalia Hochschule für angewandte Wissenschaften, Germany)
Rhoban FC (Université de Bordeaux, France)
Starkit (Moscow institute of physics and technology, Russia)
ZJUDancer (Zhejiang University, China)

Visitors are invited to the German Open from Friday (3.5) to Sunday (5.5.). There is no entrance fee for our fans (or other visitors).
More Information is available here: https://robocupgermanopen.de/en/visitors
We will continue to report from the German Open on this blog and our social media channels.

The qualified teams for the championship in RoboCup for 2019 have been published. We are happy to be one of the 20 teams which is allowed to participate in the Humanoid Kid Size League.

We are one of the 19 Teams, which is fully qualified. This means we are allowed to play in the regular games as well as in the Drop-in games. One team is only qualified to play the Drop-in games.
Drop-in games are a special game mode. In this game mode multiple teams provide one robot each to form a temporary joint team. These teams are randomly chosen. While cooperation between teams is the main focus of this form of competition, each robot is scored individually based on their performance.

The Drop-in games might be especially relevant in this year, because it is currently being voted on to use the results of the Drop-in games for the seeding for the group phase of the tournament.

In the Humanoid Teen Size League 10 teams are qualified. We are taking part in this league with our cooperation partner the WF Wolves. In this league 8 teams are fully qualified and 2 teams are only qualified for the Drop-in games.

All teams, their qualification videos, Team Description Paper as well as the robot specifications can be seen here:
https://www.robocuphumanoid.org/hl-2019/teams/

We are very excited to take part in the championship in Sydney and are very hopeful to be very successful in this year!

We have applied for the world cup of RoboCup, which will be in Sydney this year. We have written a Team Description Paper for this purpose, in which we are presenting our research of the last year. We have also made a video for our application. We are very happy to have been accepted and to be able to fly to Sydney in juli and for the exciting games we will get to play.

We are already preparing a lot for the world cup. We have had multiple integration tests in the last few months. In the integration tests we have tested all of our hard- and software. We are also already planning the next integration test.

By changing to our new robot platform, from our Minibots to our new Wolfgang robots, we have had to fight with some new problems. On the other hand we also were more motivated and that is why we managed to accomplish a lot of progress in our software as well as in the continued development of the Wolfgang hardware platform.

As part of working on our software, we have overhauled our vision. It is now better able to process and calculate the results of our CPU and graphics card in parallel.
At the moment multiple bachelor thesis are being worked on, which aim to use Fully Convolutional Neural Networks, to detect additional objects on the field. We aim to also detect robots and goalposts with this method.
The localization of our robot is being completely changed and improved right now. For this we use AMCL, a particle filter, to transform linepoints which have been found by our vision. A live demonstration video is available at: https://www.instagram.com/p/BtVlmNuFCGd/
Additionally to our localization we are also working on a world model. We aim to process the sensor input data from multiple robots and use the data to look at how the ball moved over time, which allows us to filter if we detect e.g. the ball in one place where it couldn’t possible be based on our previous measurements.
We have completely rewritten our behavior. We have developed a new description language which will allow us easier continued development of our behavior. For this we have developed our Dynamic Stack Decider and are currently working on publishing a paper about it.
For our path planning, the calculation which route our robot should take, we have switched to move_base.
For our animations and our walking we have switched to using splines. By using splines we are able to calculate smaller steps between the start of the motion and finishing the motion. This allows us to have less jerky movements.
We managed to send our motors significantly more signals per second. This way we are able to decide in more detail how the motor should move and thus accomplish a more precise movement in general.
We have built new foot sensors into our robots. By looking at the additional data we are generating this way we can stabilise our walking and our animations.
In the context of a bachelor thesis we are currently working on a system to calibrate our motors. Small inaccuracies between a measurement and the reality lead to large differences in how the robot is actually oriented. With this method large differences have already been found and fixed.

For the Symposium of the RoboCup world cup we are planning on a few more scientific papers. We will talk about these more at a later point in time and upload them on our publications page.

Our code is open source and can be viewed on Github:
https://github.com/bit-bots

We will take part in the German Open before we fly to Sydney. We will test our hard- and software there in detail and are excited for games we will get to play in Magdeburg.

Additionally to this Blog, Facebook and Twitter we will also publish images on Instagram. Our new Instagram page can be found here:
https://www.instagram.com/hhbitbots/

Our robots have gotten new foot sensors. We were inspired by Team Rhoban from Bordeaux. ( https://github.com/Rhoban/ForceFoot )

We have improved on the original implementation. The possible update rate has been improved from the original 80 Hz to a theoretically possible 9.5 kHz. Our transfer rate through the bus is limited and thus we filter the output from the load cells and only transfer with a speed of 1 kHz.

We have put the Sensors  on the top side of the feet of our robots.

We have implemented the sensors to help us gather more data and use this data to improve our walking and our animations by making them more stable.
We have put the sensors on our robots on the RoHOW. The sensors are able to measure 40 KG each without breaking. Of course we took that as a challenge and used two thumbs to create as much pressure as possible. There was not enough space near the sensors to use more than just two thumbs.

In the future we plan to scientifically compare how our measured center of pressure compares to an industrial 6 axis force torque sensor and publish our results. In the near future we also plan to release our improved version of the ForceFoot as opensource hardware.

Even though our last article was a while ago, a lot has happened. After the group phase we were eliminated from the regular competition of the world cup in a match against Rhoban from France.
We were more successful in the Drop-In Challenge: because of our stable goal keeper we managed to finish in the 4. place.

Afterwards we supported our collaboration partners in the team WF Wolves from Wolfenbüttel, who played in the TeenSize League. They finished the group phase in the third place and thus got directly to the semi final. There we cooperatively lost against the team Ichiro from Indonesia. The match for the third place against the Nubots from Australia ended in a penalty shootout which we unfortunately lost. In the Drop-In Challenge in the TeenSize League we were more successful and managed to finish in third place.

In the KidSize League Rhoban has managed to become World  Cup champion for the third time. In the TeenSize League Ichiro became the World Cup champion for the ninth time.

After the competition many members of our team spent a few more days in Canada before we went back to Hamburg. In the near future we plan to extend our cooperation with the Wolves. In September they will visit us and we will play test games and work together on scientific papers.